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During the COVID-19, wastewater-based epidemiology (WBE) has become a powerful epidemic surveillance tool widely used worldwide. However, the development and application of this technology in Chinese Mainland are relatively lagging. Herein, we for the first time monitored the community circulation of SARS-CoV-2 lineages using WBE methods in Chinese Mainland. During the peak period of infection outbreak at the end of 2022, six precious sewage samples were collected from the manhole in the student dormitory area on Wangjiang Campus of Sichuan University. RT-qPCR revealed that the six sewage samples were all positive for SARS-CoV-2 RNA. Multiplex PCR amplicon sequencing of the sewage samples reflected the local transmission of SARS-CoV-2 variants. The results of two deconvolution methods indicate that the main virus lineages have clear evolutionary genetic correlations. Furthermore, the sampling time is consistent with the timeline of concern for these virus lineages, as well as the timeline of uploading the nucleic acid sequences from the corresponding lineages in Sichuan to the database. These results demonstrate the reliability of the sewage sequencing results. Multiplex PCR amplicon sequencing is by far the most powerful analytical tool of WBE, enabling quantitative detection of virus lineages transmission and evolution at the community level.

In this study, a novel approach integrating multimodal data processing and attention aggregation techniques is proposed for pear tree disease detection. The focus of the research is to enhance the accuracy and efficiency of disease detection by fusing data from diverse sources, including images and environmental sensors. The experimental results demonstrate that the proposed method outperforms in key performance metrics such as precision, recall, accuracy, and F1-Score. Specifically, the model was tested on the Kaggle dataset and compared with existing advanced models such as RetinaNet, EfficientDet, Detection Transformer (DETR), and the You Only Look Once (YOLO) series. The experimental outcomes indicate that the proposed model achieves a precision of 0.93, a recall of 0.90, an accuracy of 0.92, and an F1-Score of 0.91, surpassing those of the comparative models. Additionally, detailed ablation experiments were conducted on the multimodal weighting module and the dynamic regression loss function to verify their specific contributions to the model performance. These experiments not only validated the effectiveness of the proposed method but also demonstrate its potential application in pear tree disease detection. Through this research, an effective technological solution is provided for the agricultural disease detection domain, offering substantial practical value and broad application prospects.

The rapid urbanization in China has significantly contributed to the vast expansion of urban built-up areas. Precisely extracting and monitoring these areas is crucial for understanding and optimizing the developmental process and spatial attributes of smart, compact cities. However, most existing studies tend to focus narrowly on a single city or on global scale with a single dimension, often ignoring mesoscale analysis across multiple urban agglomerations. In contrast, our study employs GIS and image-processing techniques to integrate multi-source data for the identification of built-up areas. We specifically compare and analyze two representative urban agglomerations in China: the Yangtze River Delta (YRD) in the east, and the Chengdu–Chongqing (CC) region in the west. We use different methods to extract built-up areas from socio-economic factors, natural surfaces, and traffic network dimensions. Additionally, we utilize a high-precision built-up area dataset of China as a reference for verification and comparison. Our findings reveal several significant insights: (1) The multi-source data fusion approach effectively enhances the extraction of built-up areas within urban agglomerations, achieving higher accuracy than previously employed methods. (2) Our research methodology performs particularly well in the CC urban agglomeration. The average precision rate in CC is 96.03%, while the average precision rate in YRD is lower, at 80.33%. This study provides an objective and accurate assessment of the distribution characteristics and internal spatial structure of built-up areas within urban agglomerations. This method offers a new perspective for identifying and monitoring built-up areas in Chinese urban agglomerations.

In this study, an innovative approach based on multimodal data and the transformer model was proposed to address challenges in agricultural disease detection and question-answering systems. This method effectively integrates image, text, and sensor data, utilizing deep learning technologies to profoundly analyze and process complex agriculture-related issues. The study achieved technical breakthroughs and provides new perspectives and tools for the development of intelligent agriculture. In the task of agricultural disease detection, the proposed method demonstrated outstanding performance, achieving a precision, recall, and accuracy of 0.95, 0.92, and 0.94, respectively, significantly outperforming the other conventional deep learning models. These results indicate the method’s effectiveness in identifying and accurately classifying various agricultural diseases, particularly excelling in handling subtle features and complex data. In the task of generating descriptive text from agricultural images, the method also exhibited impressive performance, with a precision, recall, and accuracy of 0.92, 0.88, and 0.91, respectively. This demonstrates that the method can not only deeply understand the content of agricultural images but also generate accurate and rich descriptive texts. The object detection experiment further validated the effectiveness of our approach, where the method achieved a precision, recall, and accuracy of 0.96, 0.91, and 0.94. This achievement highlights the method’s capability for accurately locating and identifying agricultural targets, especially in complex environments. Overall, the approach in this study not only demonstrated exceptional performance in multiple tasks such as agricultural disease detection, image captioning, and object detection but also showcased the immense potential of multimodal data and deep learning technologies in the application of intelligent agriculture.

Abstract Background Ulcerative colitis (UC) is a common inflammatory bowel disease, during which cell necroptosis plays key roles in driving inflammation initiation and aggravation. Previous studies reported Receptor Interacting Protein Kinase 3 (RIP3)-mediated necroptosis in multiple diseases, and RIP3 protein in Paneth cells significantly enriched in the intestines of both humans and mice. Therefore, we hypothesized targeting RIP3 to inhibit necroptosis may depress UC. Methods We classified clinical UC samples according to the modified Truelove & Witts criterion. The expression of RIP3 was measured by western blot and immunohistochemistry. Cell proliferation and apoptosis were analyzed by MTT assay and flow cytometry. ROS production and the secretion of inflammatory cytokines were measured by DCFH-DA probe and ELISA assay. TLR4/MyD88/NF-κB signaling pathway was analyzed by western blot. We established experimental colitis model in RIP3 knockout and wild-type mice and disease activity index (DAI) score was calculated. The expression and distribution of tight junction protein were analyzed by immunofluorescence. The ratio of CD4+Foxp3+ T cells in the spleen was detected by flow cytometry. Oxidative damage of mouse colon was assessed by detecting the levels of SOD, MDA and MPO. Data were analyzed by one-way ANOVA or student’s t test. Results The expression of RIP3 in human colon is positively associated with the severity of UC. RIP3 inhibitor GSK872 or RIP3 knockdown reverses the inhibitory effect of TNF-α on proliferation and the promoting effect of TNF-α on apoptosis and necrosis in human intestinal epithelial cells. In addition, RIP3 deficiency inhibits the secretion of inflammatory cytokines (IL-16, IL-17 and IFN-γ) and ROS production induced by TNF-α. In vivo, RIP3 inhibitor Nec-1 effectively improves DSS-induced colitis in mice. In mechanism, RIP3 depression could upregulate the proportion of CD4+Foxp3+ immunosuppressive Treg cells in the spleen while suppressed TLR4/MyD88/NF-κB signaling pathway and ROS generation, and all these anti-inflammation factors together suppress the secretion of inflammatory cytokines and necroptosis of intestinal epithelial cells. Conclusions This study preliminarily explored the regulating mechanism of RIP3 on UC, and Nec-1 may be a promising drug to alleviate the inflammation and necroptosis of the colon in UC patients.

Understanding the designs and performance characterizations of hot interconnects on modern data center and high-performance computing (HPC) clusters is a fruitful research topic in recent years. The rapid and continuous growth of high-bandwidth and low-latency communication requirements for various types of data center and HPC applications (such as big data, deep learning, and microservices) has been pushing the envelope of advanced interconnect designs. We believe this is high time to investigate the performance characterizations of representative hot interconnects with different benchmarks. Hence, this paper presents an extensive survey of state-of-the-art hot interconnects on data center and HPC clusters and the associated representative benchmarks to help the community to better understand modern interconnects. In addition, we characterize these interconnects by the related benchmarks under different application scenarios. We provide our perspectives on benchmarking data center interconnects based on our survey, experiments, and results.